Affinity-based biosensors exploit selective binding and interaction of certain bio-molecules (recognition probes) to detect specific target analytes in biological samples. The essential role of the biosensor platforms and the parallel and miniaturized version of them as microarrays are to exploit specific bindings of the probe-target complexes to produce detectable signals, which correlate with the presence of the targets and conceivably their abundance. The essential components of such a system include the molecular recognition layer (capturing probes) integrated within or intimately associated with a signal-generating physiochemical transducer and a readout device.
To generate target-specific signal, the target analytes in the sample volume generally first need to collide with the recognition layer, interact with the probes, bind to the correct probes, and ultimately take part in a transduction process. The analyte motion in typical biosensor settings (e.g., aqueous biological buffers) can be dominated by diffusion spreading, which from a microscopic point of view is a probabilistic mass-transfer process (modeled as a random walk for each analyte molecule). Accordingly, analyte collisions with probes become a stochastic process. Moreover, because of the quantum-mechanical nature of chemical bond forming, the interaction between the probes and the analytes molecules is also probabilistic, thus further contributing to uncertainty and noise corruption of the measured data in biosensors and microarrays. We view such phenomena as inherent noise in the detection system, which results in unavoidable uncertainties even when the measurements are noiseless. Such inherent noise is essentially inevitable since it originates from the stochastic nature of molecular-level interactions. Its examples include Poisson noise sources in microarrays and image sensor detection shot-noise.
Beside the inherent noise, other non-idealities also corrupt the signal obtained by the microarray experiments. Examples of such phenomena include probe density variations, sample preparation systematic errors, and probe saturation. We define systematic errors as the unwanted deviations from the intended detection procedure. If these errors are accurately evaluated, in theory, they can be compensated by post experiment data processing.
Gene expression microarrays are a widely used microarray platform. These systems can measure the expression level of thousands of genes simultaneously, providing a massively-parallel affinity-based detection platform in life science research. Unfortunately, the uncertainty originating from both inherent noise sources and systematic errors in each experiment can obscure some of the important characteristics of the biological processes of interest. The expression level uncertainty (overall measurement error) in microarrays, can originate from the probabilistic characteristics of detection process as mentioned before, all the way from sample extraction and mRNA purification to hybridization and fluorescent intensity measurements. Currently, there are various techniques which attempt to increase the accuracy and signal-to-noise ratio (SNR) of the estimated values. Nonetheless, these techniques often rely on comparative methods, redundant spots, or mathematical algorithms which introduce confidence zones by excluding the unreliable data and outliers. Independent of the method utilized, the degree in which the SNR can be improved in such approaches can still be limited by the inherent microarray noise and systematic errors.
The interfering signals originating from non-specific bindings in microarrays are generally referred to as “background signals.” Traditionally in microarray analysis, background signals and their fluctuations are all considered as corruptive noise without any signal content. Users often implement a sub-optimal yet widely adopted approach. This technique defines a confidence threshold level for the signal intensity in view of the background, which effectively divides the signals into irrelevant (below threshold) and relevant (above threshold) regimes. This particular approach is theoretically valid and optimal only when there is a global background signal which is constant everywhere. In practical microarray experiments, this assumption may not be not valid since the background and fluctuation level varies between spots. The approach can thus be sub-optimal. Even when local background subtraction methods are employed, the intensity data are sub-optimally processed, as the background signal that is present in the immediate vicinity of a given microarray probe spot may not actually be the same as the background signal from within the spot. The major outcome of background subtraction, regardless of the method that is used, is that the minimum detectable level (MDL) is higher than necessary. It also contributes more errors in ratio analysis approaches, since low level signals are basically truncated away. Both of these effects in turn can reduce the microarray detection dynamic range.
Beside all the uncertainties within the measurement results, there is also one major question in microarrays and all essentially affinity-based biosensor systems, and that is of the necessary incubation time (hybridization time for DNA microarrays). Since the incubation kinetics in the microarrays experiments is a function of analyte diffusion, reaction chamber size, temperature and binding kinetics of every analyte species, as well as the unknown analyte concentrations, the settling time of the system is quite complex and unpredictable. Although all these questions can, to some extent, be empirically addressed, they are still major impediments in microarray technology and platform-to-platform inconsistencies can be caused by them.
In conventional fluorescent-based microarrays and other extrinsic reporter-based (label-based) biosensors assays, the detection of captured analytes is usually carried out after the incubation step. In some cases, proper fluorescent and reporter intensity measurements are compromised in the presence of a large concentration of floating (unbound) labeled species in the incubation solution, whose signal can overwhelm the target-specific signal from the captured targets. When the incubation is ceased and the solution is removed from the surface of the array, the washing artifacts often occur that make the analysis of the data even more challenging. Thus there exists a need for affinity based sensors that are able to simultaneously obtain high quality measurements of the binding characteristics of multiple analytes, and that are able to determine the amounts of those analytes in solution.